Prediction of Humidity Depending on Temperature with Low Error Rate using regression model
Pinki Sagar1, Prinima Gupta2, Indu Kashyap3

1Ms. Pinki Sagar, Assistant Professor, Department of CSE, Faculty of Engineering and Technology, Manav Rachna Faridabad (Haryana), India.
2Dr. Prinima Gupta, Associate Professor, Department of CST, at Manav Rachna University, Faridabad (Haryana), India.
3Dr. Indu Kashyap, Department of CST, at Manav Rachna University, Faridabad (Haryana), India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1129-1133 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6095048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: This paper depicts prediction algorithm based on linear equations to forecast sequence trends in the future for time series data sets, prediction of humidity that dependent on temperature which is found in every 10 minutes. In this paper we analyze error rates during the prediction by using linear regression based algorithms. Two algorithms are discussed in this paper one for one dimensional stream data and second for two dimensional stream data. Both algorithms are applied on time series multivariate data sets and analysis of errors is to be done.
Keywords: Data Mining, Prediction, Frequent Data sets, Linear Regression, Non Linear Regression.

Scope of the Article: Data Mining